A Hybrid Ant Colony Optimization Algorithm for Job Scheduling In Computational Grids

Abstract

Grid computing links disparate computers having free resources to form a low cost infrastructure. Grid computing can provide enormous opportunities for organizations to use resources from multiple geographical locations. For efficient utilization of available resources, grid scheduling plays an important role in the grid system. Scheduling is challenging in grid due to the unique characteristics. Also, the complexity of scheduling algorithm is NP-Complete. In this study, a local search heuristic by way of multipoint mutation is introduced on the popular swarm intelligence inspired meta-heuristic, Ant Colony Optimization. Experiments show the proposed technique improves the Makespan and converges faster than conventional Ant Colony Optimization.